{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import h5py\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "from scipy.misc import imsave\n",
    "from datetime import datetime\n",
    "import model_EIGEN1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "FLAGS = tf.app.flags.FLAGS\n",
    "tf.app.flags.DEFINE_integer('max_step', 80000, 'Number of max step')\n",
    "tf.app.flags.DEFINE_integer('batch_size', 4, 'batch size')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def read_hdf5(file_name):\n",
    "    with h5py.File(file_name, 'r') as f:\n",
    "        images = np.asarray(f['images'])\n",
    "        depths = np.asarray(f['depths'])\n",
    "        infrareds = np.asarray(f['infrareds'])\n",
    "    return images,depths,infrareds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "images,depths,infrareds=read_hdf5('snow_data_test.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x=images[12]\n",
    "y=infrareds[12]\n",
    "z=depths[12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.imsave('images10.png',x)\n",
    "plt.imsave('infrareds10.png',y)\n",
    "plt.imsave('depths10.png',z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "i, j = 91, 95\n",
    "imagetest = images[i:j]\n",
    "depthtest = depths[i:j]\n",
    "infraredtest = infrareds[i:j]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def test(images,infrareds,global_layers,local_layers,scaling_factor,is_training):\n",
    "    with tf.Graph().as_default():\n",
    "        logits = model_EIGEN.inference(images,infrareds,global_layers,local_layers,scaling_factor,is_training)\n",
    "        dim = logits.get_shape()[3].value\n",
    "        true_labels = tf.reshape(depthtest, [-1])\n",
    "        true_labels = tf.cast(true_labels, tf.int64)\n",
    "        pre_dep = tf.reshape(logits , [-1, dim])\n",
    "        cross_entropy =  tf.nn.sparse_softmax_cross_entropy_with_logits(\n",
    "            logits=pre_dep, labels=true_labels, name='cross_entropy')\n",
    "        cross_entropy_mean = tf.reduce_mean(cross_entropy,name='cross_entropy_mean')\n",
    "        saver = tf.train.Saver()\n",
    "        with tf.Session() as sess:\n",
    "            saver.restore(sess, 'saver/model.ckpt-100000')\n",
    "            labels = sess.run(logits)\n",
    "            loss= sess.run(cross_entropy_mean)\n",
    "    return labels,loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from saver/model.ckpt-100000\n"
     ]
    },
    {
     "ename": "NotFoundError",
     "evalue": "Key RDB/fc6_VI/weights not found in checkpoint\n\t [[Node: save/RestoreV2_65 = RestoreV2[dtypes=[DT_FLOAT], _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"](_arg_save/Const_0_0, save/RestoreV2_65/tensor_names, save/RestoreV2_65/shape_and_slices)]]\n\nCaused by op 'save/RestoreV2_65', defined at:\n  File \"D:\\python\\lib\\runpy.py\", line 193, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"D:\\python\\lib\\runpy.py\", line 85, in _run_code\n    exec(code, run_globals)\n  File \"D:\\python\\lib\\site-packages\\ipykernel_launcher.py\", line 16, in <module>\n    app.launch_new_instance()\n  File \"D:\\python\\lib\\site-packages\\traitlets\\config\\application.py\", line 658, in launch_instance\n    app.start()\n  File \"D:\\python\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 477, in start\n    ioloop.IOLoop.instance().start()\n  File \"D:\\python\\lib\\site-packages\\zmq\\eventloop\\ioloop.py\", line 177, in start\n    super(ZMQIOLoop, self).start()\n  File \"D:\\python\\lib\\site-packages\\tornado\\ioloop.py\", line 888, in start\n    handler_func(fd_obj, events)\n  File \"D:\\python\\lib\\site-packages\\tornado\\stack_context.py\", line 277, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"D:\\python\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 440, in _handle_events\n    self._handle_recv()\n  File \"D:\\python\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 472, in _handle_recv\n    self._run_callback(callback, msg)\n  File \"D:\\python\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 414, in _run_callback\n    callback(*args, **kwargs)\n  File \"D:\\python\\lib\\site-packages\\tornado\\stack_context.py\", line 277, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"D:\\python\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 283, in dispatcher\n    return self.dispatch_shell(stream, msg)\n  File \"D:\\python\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 235, in dispatch_shell\n    handler(stream, idents, msg)\n  File \"D:\\python\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 399, in execute_request\n    user_expressions, allow_stdin)\n  File \"D:\\python\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 196, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"D:\\python\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 533, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"D:\\python\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2698, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"D:\\python\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2802, in run_ast_nodes\n    if self.run_code(code, result):\n  File \"D:\\python\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2862, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-17-d41f48b512fc>\", line 3, in <module>\n    labels,loss = test(test_infrared,test_image,3,3, 1,False)\n  File \"<ipython-input-13-5f2fed7197e2>\", line 11, in test\n    saver = tf.train.Saver()\n  File \"D:\\python\\lib\\site-packages\\tensorflow\\python\\training\\saver.py\", line 1218, in __init__\n    self.build()\n  File \"D:\\python\\lib\\site-packages\\tensorflow\\python\\training\\saver.py\", line 1227, in build\n    self._build(self._filename, build_save=True, build_restore=True)\n  File \"D:\\python\\lib\\site-packages\\tensorflow\\python\\training\\saver.py\", line 1263, in _build\n    build_save=build_save, build_restore=build_restore)\n  File \"D:\\python\\lib\\site-packages\\tensorflow\\python\\training\\saver.py\", line 751, in _build_internal\n    restore_sequentially, reshape)\n  File \"D:\\python\\lib\\site-packages\\tensorflow\\python\\training\\saver.py\", line 427, in _AddRestoreOps\n    tensors = self.restore_op(filename_tensor, saveable, preferred_shard)\n  File \"D:\\python\\lib\\site-packages\\tensorflow\\python\\training\\saver.py\", line 267, in restore_op\n    [spec.tensor.dtype])[0])\n  File \"D:\\python\\lib\\site-packages\\tensorflow\\python\\ops\\gen_io_ops.py\", line 1020, in restore_v2\n    shape_and_slices=shape_and_slices, dtypes=dtypes, name=name)\n  File \"D:\\python\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\", line 787, in _apply_op_helper\n    op_def=op_def)\n  File \"D:\\python\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 2956, in create_op\n    op_def=op_def)\n  File \"D:\\python\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 1470, in __init__\n    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access\n\nNotFoundError (see above for traceback): Key RDB/fc6_VI/weights not found in checkpoint\n\t [[Node: save/RestoreV2_65 = RestoreV2[dtypes=[DT_FLOAT], _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"](_arg_save/Const_0_0, save/RestoreV2_65/tensor_names, save/RestoreV2_65/shape_and_slices)]]\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNotFoundError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32mD:\\python\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[1;34m(self, fn, *args)\u001b[0m\n\u001b[0;32m   1322\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1323\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1324\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\python\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[1;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[0;32m   1301\u001b[0m                                    \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1302\u001b[1;33m                                    status, run_metadata)\n\u001b[0m\u001b[0;32m   1303\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\python\\lib\\site-packages\\tensorflow\\python\\framework\\errors_impl.py\u001b[0m in \u001b[0;36m__exit__\u001b[1;34m(self, type_arg, value_arg, traceback_arg)\u001b[0m\n\u001b[0;32m    472\u001b[0m             \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc_api\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_Message\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 473\u001b[1;33m             c_api.TF_GetCode(self.status.status))\n\u001b[0m\u001b[0;32m    474\u001b[0m     \u001b[1;31m# Delete the underlying status object from memory otherwise it stays alive\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNotFoundError\u001b[0m: Key RDB/fc6_VI/weights not found in checkpoint\n\t [[Node: save/RestoreV2_65 = RestoreV2[dtypes=[DT_FLOAT], _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"](_arg_save/Const_0_0, save/RestoreV2_65/tensor_names, save/RestoreV2_65/shape_and_slices)]]",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mNotFoundError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-17-d41f48b512fc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mtest_image\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimagetest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m256\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m512\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mtest_infrared\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minfraredtest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m256\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m512\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mloss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_infrared\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtest_image\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mpred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-13-5f2fed7197e2>\u001b[0m in \u001b[0;36mtest\u001b[1;34m(images, infrareds, global_layers, local_layers, scaling_factor, is_training)\u001b[0m\n\u001b[0;32m     11\u001b[0m         \u001b[0msaver\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSaver\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0msess\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m             \u001b[0msaver\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrestore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msess\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'saver/model.ckpt-100000'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     14\u001b[0m             \u001b[0mlabels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlogits\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m             \u001b[0mloss\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0msess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcross_entropy_mean\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\python\\lib\\site-packages\\tensorflow\\python\\training\\saver.py\u001b[0m in \u001b[0;36mrestore\u001b[1;34m(self, sess, save_path)\u001b[0m\n\u001b[0;32m   1664\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0min_graph_mode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1665\u001b[0m       sess.run(self.saver_def.restore_op_name,\n\u001b[1;32m-> 1666\u001b[1;33m                {self.saver_def.filename_tensor_name: save_path})\n\u001b[0m\u001b[0;32m   1667\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1668\u001b[0m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_build_eager\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msave_path\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbuild_save\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbuild_restore\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\python\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m    887\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    888\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[1;32m--> 889\u001b[1;33m                          run_metadata_ptr)\n\u001b[0m\u001b[0;32m    890\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    891\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\python\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run\u001b[1;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m   1118\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1119\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[1;32m-> 1120\u001b[1;33m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[0;32m   1121\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1122\u001b[0m       \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\python\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_run\u001b[1;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m   1315\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1316\u001b[0m       return self._do_call(_run_fn, self._session, feeds, fetches, targets,\n\u001b[1;32m-> 1317\u001b[1;33m                            options, run_metadata)\n\u001b[0m\u001b[0;32m   1318\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1319\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\python\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[1;34m(self, fn, *args)\u001b[0m\n\u001b[0;32m   1334\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1335\u001b[0m           \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1336\u001b[1;33m       \u001b[1;32mraise\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnode_def\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1337\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1338\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_extend_graph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNotFoundError\u001b[0m: Key RDB/fc6_VI/weights not found in checkpoint\n\t [[Node: save/RestoreV2_65 = RestoreV2[dtypes=[DT_FLOAT], _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"](_arg_save/Const_0_0, save/RestoreV2_65/tensor_names, save/RestoreV2_65/shape_and_slices)]]\n\nCaused by op 'save/RestoreV2_65', defined at:\n  File \"D:\\python\\lib\\runpy.py\", line 193, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"D:\\python\\lib\\runpy.py\", line 85, in _run_code\n    exec(code, run_globals)\n  File \"D:\\python\\lib\\site-packages\\ipykernel_launcher.py\", line 16, in <module>\n    app.launch_new_instance()\n  File \"D:\\python\\lib\\site-packages\\traitlets\\config\\application.py\", line 658, in launch_instance\n    app.start()\n  File \"D:\\python\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 477, in start\n    ioloop.IOLoop.instance().start()\n  File \"D:\\python\\lib\\site-packages\\zmq\\eventloop\\ioloop.py\", line 177, in start\n    super(ZMQIOLoop, self).start()\n  File \"D:\\python\\lib\\site-packages\\tornado\\ioloop.py\", line 888, in start\n    handler_func(fd_obj, events)\n  File \"D:\\python\\lib\\site-packages\\tornado\\stack_context.py\", line 277, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"D:\\python\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 440, in _handle_events\n    self._handle_recv()\n  File \"D:\\python\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 472, in _handle_recv\n    self._run_callback(callback, msg)\n  File \"D:\\python\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 414, in _run_callback\n    callback(*args, **kwargs)\n  File \"D:\\python\\lib\\site-packages\\tornado\\stack_context.py\", line 277, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"D:\\python\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 283, in dispatcher\n    return self.dispatch_shell(stream, msg)\n  File \"D:\\python\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 235, in dispatch_shell\n    handler(stream, idents, msg)\n  File \"D:\\python\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 399, in execute_request\n    user_expressions, allow_stdin)\n  File \"D:\\python\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 196, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"D:\\python\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 533, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"D:\\python\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2698, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"D:\\python\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2802, in run_ast_nodes\n    if self.run_code(code, result):\n  File \"D:\\python\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2862, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-17-d41f48b512fc>\", line 3, in <module>\n    labels,loss = test(test_infrared,test_image,3,3, 1,False)\n  File \"<ipython-input-13-5f2fed7197e2>\", line 11, in test\n    saver = tf.train.Saver()\n  File \"D:\\python\\lib\\site-packages\\tensorflow\\python\\training\\saver.py\", line 1218, in __init__\n    self.build()\n  File \"D:\\python\\lib\\site-packages\\tensorflow\\python\\training\\saver.py\", line 1227, in build\n    self._build(self._filename, build_save=True, build_restore=True)\n  File \"D:\\python\\lib\\site-packages\\tensorflow\\python\\training\\saver.py\", line 1263, in _build\n    build_save=build_save, build_restore=build_restore)\n  File \"D:\\python\\lib\\site-packages\\tensorflow\\python\\training\\saver.py\", line 751, in _build_internal\n    restore_sequentially, reshape)\n  File \"D:\\python\\lib\\site-packages\\tensorflow\\python\\training\\saver.py\", line 427, in _AddRestoreOps\n    tensors = self.restore_op(filename_tensor, saveable, preferred_shard)\n  File \"D:\\python\\lib\\site-packages\\tensorflow\\python\\training\\saver.py\", line 267, in restore_op\n    [spec.tensor.dtype])[0])\n  File \"D:\\python\\lib\\site-packages\\tensorflow\\python\\ops\\gen_io_ops.py\", line 1020, in restore_v2\n    shape_and_slices=shape_and_slices, dtypes=dtypes, name=name)\n  File \"D:\\python\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\", line 787, in _apply_op_helper\n    op_def=op_def)\n  File \"D:\\python\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 2956, in create_op\n    op_def=op_def)\n  File \"D:\\python\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 1470, in __init__\n    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access\n\nNotFoundError (see above for traceback): Key RDB/fc6_VI/weights not found in checkpoint\n\t [[Node: save/RestoreV2_65 = RestoreV2[dtypes=[DT_FLOAT], _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"](_arg_save/Const_0_0, save/RestoreV2_65/tensor_names, save/RestoreV2_65/shape_and_slices)]]\n"
     ]
    }
   ],
   "source": [
    "test_image = np.reshape(imagetest, [4, 256, 512, 3])\n",
    "test_infrared = np.reshape(infraredtest, [4, 256, 512, 3])\n",
    "labels,loss = test(test_infrared,test_image,3,3, 1,False)\n",
    "print(loss)\n",
    "pred = np.argmax(labels, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "log = 0\n",
    "for i in range (5):\n",
    "    imagetest = images[(i*4) : ((i+1)*4)]\n",
    "    depthtest = depths[(i*4) : ((i+1)*4)]\n",
    "    \n",
    "    test_image = np.reshape(imagetest, [4, 256, 512, 3])\n",
    "    \n",
    "    labels = test(test_image,3,3, 1,False)\n",
    "    d = np.reshape(depthtest, (-1))+1\n",
    "    d_p = np.reshape(pred, (-1))+1\n",
    "    log+= np.sum(np.abs(np.log10(d) - np.log10(d_p)))\n",
    "    \n",
    "print(log/ (4*256*512*5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wi2xeU6uO9XbLziIdaY9vNTm/NrFhhD5XDjRv5duJxhBWu+38tpzlXXghiOr6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fc08dfe6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(221)\n",
    "plt.imshow(depthtest[1],cmap='jet')\n",
    "plt.axis('off')\n",
    "plt.subplot(222)\n",
    "plt.imshow(pred[1],cmap='jet')\n",
    "plt.axis('off')\n",
    "plt.subplot(223)\n",
    "plt.imshow(depthtest[3],cmap='jet')\n",
    "plt.axis('off')\n",
    "plt.subplot(224)\n",
    "plt.imshow(pred[3],cmap='jet')\n",
    "plt.axis('off')\n",
    "#plt.savefig('80000nonpyramid1000-1004.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JhMR5ePn0Gc+/+oqyLPnkk0949PiUzz77DGMMm82a5XLZXc/h70H0EVHGIoqQ\nwrbB2a7lqzUsb28pZI1whrKYc3x0xs9fbFiv16HS2HS9g1AIAbNpgfOhACrPc05mxz3yaZqmYzSD\nI/jgnz59yqNHjzg5Pma5XPbCPNzL5k52SHg2FqU0X3zxHcqy5Ec/+hHr9Zosy2jb4Ob57LPPWCwW\nnJ+fs9lIqm3bBXCDElA6Q6DxXmCtYD47YVLOmR+dcvroPcgLpOkEszU0TU1VbzhvWn74n/6SP//z\nP+f68oosKzB1S2Mc243BeYETltbskJfrCtnCrcjumtF9Z5P7ul8wpot2iLb3rFS44+7SWo8i20Ou\nx2EAUEqJbccFxNCVkFoHqbAdZuEM7y9+N/SnCyH6Ev672UJ33RjRNRGPjwBijF7nfjmkMCPqTt1G\nUdimsZF0nOm54zMfeiXSOYqf7bLG7lIq7wI484QGjQLnd2mvr+t9dYjeCUE/zGONk5QyRBQyVbVh\nvQkCsG3r7juNkhnzOWS6QCnAJ+Y2scJM7j3sIVIYY7qUrLO7LIhubHVds622uDakQmZZRtudQ2uN\nKEt8G4KRMaXMew8uFAF559hut7x48YLW1CHo2PnfYlD3voeaCpfQ/TLbW/Th+27+LORadRleio8+\n+ojpdMYHH3zAk6+fB6Tc9RgKiyzD2GCfxDFt66pXsHVd09UT9UpJJVk/QF91GSluzBGOCQx8fHxM\nURTUVUtTh8UVYh4Fi8WMk5Mjbm5ukDLM413XgUYgcdZT1wbTOhaLBZQZIBB5hgCef/2EzWrNi5fP\nWFvHz//+Ky7Orzk/vwptpVUZFpkJ8+MIQbFU0A1pHxnfFbiHnt8hFJ0KkAhO0vXwtoh2KFBhJ7iH\nAcRDwjJVHm8qYKK1M7RM0zV9yOLos6+i+27gKnkTGoLG+7wFY7+NLqY3sVyGqPw+a2msBgEOVxYD\nfQvqNI3zH2UefYra08kaZi40myXrm2uePfsG5w1t23QCXJPpgk8++YxscUKe6dCRpv/t3QcdmVBk\nuykYauk7ZKJ/Ufa+4jzPWW+W3N6uads6BAClwDYtTbVCC0FOMMLaqsYZAwjw4FuLyoLiuL6+ZrUO\n/u3go5T7/dAPMGVEHqE52YLpdIoQce52mSFZVuBsjXOWv//7n1Nt1/zO7/8xZVny+effZrWscQ6+\n/OpJ5wsHcF1PnWDxzOdzvIDb2yXee6bTKcWkDEjDhBYPr1696pk2Vrlqrdlut9zc3OylmRrbwBae\nPn0adtxCoJRms644PVswn08REm5ublgul2y3oWd+eE5drnIIslDXLdfXt/zoL/6Wi8sNJ48/YP7e\nGQBtY3jx9AX/75/+gNvrFT/cJNXaAAAgAElEQVT58U94tlzz4sULpMxQ+qgLqAt8574ShGBx21l6\nO6QdAmVBEKTPYYcIUxpbuIdcWkMhEhaz3tt56pC1eZ8AHloSStzdq2EYHBRCIMVO+AwF9hil409R\nciowx/zwQ1//sAYh7RY5Fjsbm8OhEkl94ul4xtzGENb1dDrtW3+kMmFMeR2yhlJqk+LModsqftbv\nUiUCSAGQajf2VMi/TbrpOyHoU7803J3InrxF4MBbvLXgPEpKbGuQCHDheykIW/bFSWRfg+9cOAKT\npFK9TtArMTzPPkKKpnXtgluhqiryDgmnGlm40JbBddeJD6xtW66urjp/vep3mekueu8cSil7oTpE\nIgGlmtB1sduQZLOu+PGPf4z3vhfOMWfee4/xprdKYupeap7OZjOEUKisS+tLlHJaSp+mqqWLVUqJ\n9OGa5+fnTCdVyBSqqv66AJeXl+BF34clMnjbRleJx3sLQuJcw9XlktkiKMy23aKU4Prmhpcvn3N7\nfcvqZsNmWXF1uWSzbtHa41HILuDrIZSqs7+JyBg/DvlgiOjuswKGguCQJRCPTesjxug+dJqi2jFL\ndpjNsxOGOyH4JoHQ9B4OKbb0/scQMyT+bX/XtXXfde/7PF7rTVJDhRAsFgvm83nHR7vU50M8kN5H\nKsRTt1sU4un/x64tRAAv0UIcs7RSy+hN6J0Q9Kenp6zX6zttStNcYADb1AhnybWitg2ZEuR5aGYm\npURLiRIe1xp8uucsO/9WoKT8OHHnDNHHGAV0Zfo9OqOLpdnKHqUUWUBh0d2hlMJZG3KiG0vTGoQI\n1bRV13q5KAryImyAEdIet2y32x0COqC9UwWT5127VPYLs7wPRUU5lqapmM+OKMuS/+f73+fi4oJv\nnr1iOlkEX31sDdGhqKPjOYvFAle34RmZkFL5xRdfMJ8fcXF1GbpONp3bqmm4ubnpUzejYO570Ltd\nUytjwzO4ubmhrlqqqqFpQjvlLMvYbres1jc0tUWpDCk1zrbsGs9B0wR+ETJYCjfXG2ZHt7x49RI9\nF2SZ4t9//z9ydb7k6ZNLrl4tuThfst40ZHqCI7RLbm0Q8s47nGlx0oPfr8QczntK6YI+JBAOob74\nnlpv8XzDati3CcCl594bm9tHsnFnr1TwSymJfrmhMLsvRpDenx0BUek44nHpWNJ88aY2e+6qsQDn\ncAyH3EEpKIO7CSDD39d1zWKx2LM4UvmQzks8X+TxVKAPBX06N8CdzUXiuIzz0O15ERF9PDbEDiRK\n/SNravbiyx9jEaA0upjjpcJLhbMhEIEQWOkwWmC0ANllg3iHlsHnrFSGF4LWg+0Qf5zQslSdO6Pt\nJrRF+q6VcTUBdr3S46JKq2Ajeb8Ki0CA8QYlBI1ztI0FmeMIr2Nl0ErghETT7SKjNeWspLEN26vb\nkJOPQKDItGQ+K5nNZhwfH7Narfjyyy/DtbotEWObCNmVhSshoOuF71CQTbCqYO0EefY+WoCwBiks\n7WaLzDTWGNRkxgbNPD+ikZqNA6sEl+uwk5LxBqkk3psQBFIzvHBkpSQrZlxeXrLaLlGZQmWC+dGM\nvMxo2xaVZVy9umQ6n1BVIvTx8aGPvtYaLxzWG6QQWOdo2xBY1FmGl4bV9hJjDSqfIWSOkILtylFO\npkFpE1KknLcIKcIcdpt2tya46G7tDZNN2LdXe83liytePb3i8uKW22XNxc2Sy9WSll3Plf75OhMs\nJ+GIexn0QYg7vLDvDonvfQLAPVk3Y6hy7PxRUaZprsNjxhBh+r7bMa3LLfOhSZ7tWitIKWlM26NI\nhMB6h7XuTifFoXAcXj+9r9SlOKb8htlLqRurt/qUR0jwXYuGmNQUG93tBPPd86QAKB3vsPgsbeOQ\n3kPbtjx79qy3LlPhfKeeQGR4Eep4rBfQjV8I19feSAlt24xYVbsitDDf0fLIiNmDqtuaFK/RKlq1\nYce9N6V3QtD/4Ac/QOUFR6dnfPFPfp+s1N2mDrutupzztE3IDvE+ZNK4zqSy1uOqmpOmoezMbue7\nBywsxu78ulE7uy71MNOdhvUmFL6YuxkT0KEsH1oVB1QkkSJDSRvSOru/M10ilUdpQV448lxzdnqM\nUoqmsaw3DUKX4BybzYpMT7A27EhVFEWPaPp9ZGMwuhNouN14YhZozG//1re+hcw0i+OPKYqM6SRn\nu1nx47/8M9ZLj7MNHnj29AWvXl7w/NlLbm5uMa3DWTCmaxDnCAVAQjMpZ8ymC04XR2zqiqZ5SZ6X\nXF/f8OTJN6FauSg4OjpCiKy3LKLpG3Pnvfcsl8u9TIyYe9w0DbQ71BJTLIUQHB0d4bzpEWdqtg5N\nYO89t8stQl7x/MU5H/3Wx0hVUDeG9abi1cUF17e3tNbgxP2Nue5DjocoReSHkP3b0qHfp+6QMRdS\npLHP4v2l440CLQ3UDs97H40Fc9ONMYbulxTxDht4peg7FcK787+5gLtv/g8p4jje9PevO88+78Sx\n7ltwSu0KnLTeAcvU5dMfm+3mLt20Zc/1+Y/RR08SZIjm5U7QB63dowAhku/oTPkd+LLWYlzGbDaD\nbGfyhTL1uNlz6CJpxa6AIzW5RHKN/kGKHIRA6bBjEiJHZ1M8QSgfHx9zdHTE8WxCXYc0vjzPOVpM\nQ+qiUDSt5fLyCuc9Socuj9G3HtMUo5CPiw/AMWh57Hf+99lkwuPHj/n888/RRd4J+oJMep49e0pd\nt7SNQSDJVfBv13XL7e2SqqrxHpTSeL+rUozd+6x1NE3L7e0t621FXTWdL70J+966Bu8Fk0mImWy3\noS9NlmV92+HVatX7/9P0QO+DS2e1WoHYdzcFRlYomVE3+1slxvtOF0Z8tVaw2tT85Kd/Tz4J87ve\n1lStYVvXNNZ0+woc9iWnlKLBQ8dEGgqyQ8e/rfB/ndvoPsE+dmx6L0NBOvw+nff7FNd9bpOxv6Ni\niAJrbO7unC8R9PtjHB3S6P0PLbBDwv5tlH56HzFTSkqJkDuZIiVg7/Jceu1ham0698PElLfloXdC\n0EspcQR/5NXVFUKHLeOc9VgXAq1CeKRSgOz2HXUY51ECnA2bXBvjqFtLmRUcHX3M559/zmw2Yz6f\nA4Rt6jpk2DfvN8seRWute1QdBdJexN+H6Yq+cAhVn0NFoZzG+9BxTkqBdw3OGT748FNOH3/Ek+ev\nuLm6RKkS22z20FVEqql/01oLct+nqzofqvGO4+NjPv30U7744gu8FGh9htaSp0++5puvnvDs6SuK\nTJArgXCeb56dh/GRkelgpjrrsEZg2tAiWsoMPFxe3HJ9tUI6T+ssTWMoS011E5RYU9eYtqLIa4rp\njGrbsFqtECKkTWodsmgATk/C1nzX19ehAMqFWoi6btCZZLFYsNlsKIqC+XyOECEldFute0aPcZBI\nQ39xYzVm6/jBD/+Kr5+9YDab0raGattQ2RaDxymPSFxzqbsF7mbFpJ/dt8AOuTSG9DaLdAzRDoXT\nIaF6SAFEXk2V6lBQx2u/ToC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1EoXnXUG/j0APXyt+HxfbsEgrRcOhAVrqI039sG+2wfRd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\nVb5zWYF+lVVWWeU7l/8H14pmwKRPVKQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fc0cd4b6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(imagetest[1])\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
